• Started creating Face Tracking demo
• Look into how to add features, track certain face elements, …
• Combining Face Tracking with AR, ML, … (https://towardsdatascience.com/real-time-3d-face-tracking-with-deep-learning-963b91bb5ad4, https://medium.com/datadriveninvestor/machine-learning-on-facial-recognition-b3dfba5625a7)
• Found really interesting blogpost on AR tracking of objects (https://medium.com/dev-genius/play-video-with-arkit-and-image-tracking-e8b52dc4ad76)

• Started looking at Face Tracking
• Followed tutorials on face tracking, recognition using ARKit (https://developer.apple.com/documentation/arkit/tracking_and_visualizing_faces, https://www.youtube.com/watch?v=2CQDV2SOyOo, https://www.novatec-gmbh.de/en/blog/arkit-face-recognition-ios/)

• Finalized createML demo (https://github.com/jaspervermeulen/ImageClassifier)
• Pushed everything to github and closed project

“Machine learning focuses on applications that learn from experience and improve their decision-making or predictive accuracy over time.” — IBM

Machine learning is an easier and smarter way to analyse data, this also means you can produce accurate results and analysis. In this short demo I’m going to show you…

• Worked more on custom machine learning models / classifiers (PlantsClassifier and DrinkClassifier)
• Followed tutorials on createML (https://www.youtube.com/watch?v=a905KIBw1hs, https://www.youtube.com/watch?v=x9_BG2q7XYw&t=2557s)

• Started looking at createML (https://developer.apple.com/documentation/createml)
• Started creating custom machine learning classifier and implementing it
• Used standard project to test out these classifiers, own ml models

• Completed MNIST demo (https://github.com/jaspervermeulen/CoreMLMNIST)
• Looked intro other models to experiment with

• Looked into CoreML (https://www.apple.com/benl/macos/big-sur/)
• Followed online tutorials CoreML and different machine frameworks
• Looked at different Models, premade and how to make them yourself (createML)
• Started developing CoreML demo (MNIST)